Low rank and range recursive STAP using a Taylor series expansion

In this paper, we test and compare, in the case of the signals given by the Direction Generale de l'Armement Maitrise de l'Iinformation (DGA/MI), some algorithms recently proposed by the authors to compensate clutter in order to detect, from an airborne radar, targets slowly moving on the ground. The proposed algorithms make use of i) rank reduction in order to reduce the number of secondary snapshots necessary to estimate the data covariance matrix compared to the classic SMI method; ii) a Taylor series expansion of the clutter subspace in order to compensate for a possible range non stationarity of the data; in) a range recursivity in order to reduce the computational complexity. It appears that the proposed algorithms yield a good performance with a computational complexity which is linear with respect to the number of parameters.